Performance Comparison of Machine Learning Methods for Customer Churn Prediction in Telecom

Abstract

Due to a high competition in the market, the telecom operators are affected by churn, therefore it is very important for them to identify which users are likely to leave them and switch to the competition telecom company. This research uses data on behaviour of the users from telecom systems that serve to identify patterns in behaviours and thereby recognize the churn. At preparing data, a selection of useful attributes was made using the Principal Component Analysis (PCA). The normalization of the attribute values has also been made in order to obtain a proper balance of the influence of all the attributes. Several prediction models for detecting the churn of the Prepaid users in the telecom were created in this paper, and a performance analysis of the implemented Data mining models was performed.